Distributed LLM Manager

What It Does

Installation

pip install ray grpcio pyyaml

Usage

python distributed_llm_manager.py --config /path/to/config.yaml

Source Code

import argparse
import yaml
import ray
import grpc
import numpy as np
from concurrent import futures

class DistributedLLMManager:
    def __init__(self, config_path):
        self.config_path = config_path
        self.config = self._load_config()

    def _load_config(self):
        try:
            with open(self.config_path, 'r') as file:
                return yaml.safe_load(file)
        except FileNotFoundError:
            raise FileNotFoundError(f"Configuration file '{self.config_path}' not found.")
        except yaml.YAMLError as e:
            raise ValueError(f"Error parsing YAML configuration: {e}")

    def setup_cluster(self):
        ray.init(address=self.config.get('ray_address', 'auto'))
        print("Ray cluster initialized.")

    def partition_model(self):
        model_size = self.config['model']['size']
        num_shards = self.config['model']['shards']
        if num_shards <= 0:
            raise ValueError("Number of shards must be greater than 0.")
        shard_size = model_size // num_shards
        print(f"Model partitioned into {num_shards} shards of size {shard_size}.")
        return shard_size

    def start_inference_service(self):
        executor = futures.ThreadPoolExecutor(max_workers=10)
        server = grpc.server(executor)
        print("gRPC server started for inference.")
        return server

    def run(self):
        try:
            self.setup_cluster()
            shard_size = self.partition_model()
            server = self.start_inference_service()
            print("Distributed LLM Manager setup complete.")
        except Exception as e:
            print(f"Error during setup: {e}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Distributed LLM Manager")
    parser.add_argument('--config', required=True, help="Path to the model configuration file.")
    args = parser.parse_args()

    manager = DistributedLLMManager(args.config)
    manager.run()